为进一步厘清不同出租车路径选择行为的差异性，采用频繁序列挖掘方法提取了同一个OD对间的频繁路径，构建路径选择集，分别从静态和动态两个角度分析路径集的相似特性。以西安市出租车的轨迹数据为研究对象，通过栅格划分与路网匹配，获得了不同OD对之间的路径集合。重新定义了频繁路径，采用PrefixSpan演变算法，在得到频繁子序列的基础上引入动态阈值和频繁度指标挖掘频繁路径，提取了最短路径和其他路径，完成了3类有效路径集的构建，并分析了路径集的一般属性。其后，将路径上二维时间序列（轨迹）间的相似度表示为动态相似度，将一维有向序列（路段）间的相似度表示为静态相似度，基于改进的最长公共子序列和动态时间规整算法对3类路径进行了相似性分析。结果表明：频繁路径与最短路径的相似度较高，意味着大多数出租车仍然选择具有最低出行时间的路段，但不一定会选择最短路径；时间和距离仍是出行者选择路径时主要考虑的因素，但出行者并不完全追求时间最短或距离最短；试验得到的动态相似度计算结果显著高于静态相似度计算结果，说明路径上的二维时序相似度高于一维形状相似度；两种方法下频繁路径和最短路径的相似度均最高，最短路径和其他路径的相似度均最低，比较结果的一致性说明可以用动态轨迹的相似度来大致度量静态路径的相似度。文中的频繁路径挖掘算法具有一定的可靠性，可为城市交通管理者进行路径推荐、道路规划等提供支持。

In order to further clarify the differences in routing behaviors of different taxis, this paper adopted the method of frequent sequence mining to extract the frequent path between the same OD pairs, construct path sets, and analyze the similar characteristics of path sets from static and dynamic perspectives. By taking the trajectory data of taxis in Xi’an City as the research object, the path set between OD pairs is obtained through grid division and road network matching. Then, the frequent path is redefined, the PrefixSpan evolution algorithm is adopted, and the dynamic threshold and frequency index based on the obtained frequent subsequences are introduced to mine frequent paths. Furthermore, in order to complete the construction of three kinds of effective path sets, the shortest path and other paths are extracted, and the general properties of the constructed path sets are analyzed. Finally, the similarity between two-dimension time series (tracks) on the path is represented as dynamic similarity, and the similarity between one-dimension directed sequences (sections) is represented as static similarity, and the similarity analysis of three types of paths is carried out based on the improved longest common subsequence and dynamic time regularity algorithm. The results show that: (1) the similarity between the frequent path and the shortest path is rather high, meaning that most taxis still choose the road with the lowest travel time but not the shortest path; (2) time and distance are still the main considerations for travelers when choosing a path, but travelers do not completely pursue the shortest time or distance; (3) the calculated dynamic similarity is significantly higher than the static similarity, which means that the two-dimension sequential similarity on the path is higher than the one-dimension shape similarity; and (4) the two proposed methods both possess the highest similarity between the frequent path and the shortest path and the lowest similarity between the shortest path and other paths The consistency of the comparison results indicates that the similarity of the static path can be roughly measured by the that of the dynamic trajectory. The proposed frequent path mining algorithm is of certain reliability. It can provide supports for urban traffic managers with recommend routes and planed roads.